The operation of gas turbine engines will, in time, lead to a decrease in efficiency due to wear and damage as well as other factors. Because the rate of deterioration depends on a varied of operational factors, the actual rate for an individual engine is very difficult to predict. Accordingly, engine components are scheduled for maintenance based on a predetermined number of hours or cycles of operation. This maintenance program selects the time for either inspection or overhaul or both based upon factors such as past experiences. If a component actually fails before the expected time, tests are made to revise the routine for this part.
Efforts have been made to estimate the reliability remaining in an engine of this type, and sensors are employed to provide data on which to monitor and determine engine operational conditions and expected life before repair. While this is appropriate for statistically large numbers, because individual components vary because of manufacturer's tolerances, deterioration due to time or wear, and the effect of one failing or weakened part on other parts, no theoretical estimate other than one of extreme conservative limits of use would be accurate for all the engines of any given type.
Prior art efforts to resolve this dilemma have not been successful. U.S. Pat. Nos. 6,466,858 and 6,532,412 and their Patent Application Publication No. US 2002/0193933 to Adibhatla et al all relate to a technique of calculating reference parameters (e.g. component efficiency) of the engine at any given time and trending them for monitoring their health. In the Adibhatla et al patents and publication, a parameter estimate algorithm (Kalman filter, or regression) is suggested to be used for trending, which is said to be useful in fault diagnosis and isolation. Even though, this approach works in theory, it could be very difficult estimation problem.
U.S. Pat. No. 5,018,069 to Pattigrew uses simple empirical correlations and has to correct the data to standard operating conditions, then compare the data with nominal data. Due to the absence of a rigorous model, in addition to the sensor information, it uses various calculated parameters such as egt vs. fuel flow for fault diagnosis.
U.S. Pat. No. 5,951,611 to La Pierre is similar to Pattigrew and is based on online data trends. It discloses a data driven technique. It also uses different trend parameters where shifts are identified. These shifts are not mapped with the real life faults. With the exception of performance loss, the fault descriptions are not precise.
U.S. Pat. No. 6,408,259 to Goebel et al. describes a data based anomaly detection method, which uses a fuzzy KNN (k nearest neighbor) algorithm on preprocessed sensor data and transformed data to classify operation data as normal or abnormal data. Goeble et al. does not address fault diagnosis and is not model based.
U.S. Pat. No. 6,591,182 to Cece et al. provides a manual for decision-making process for diagnostic trend analysis using an aircraft engine as an example. Cece et al. uses an approach that is data driven and uses various thresholds (similar to fuzzy logic) to diagnose the faults.
U.S. Patent Application Publication No. US2003/0167111 to Oscar Kipersztok et al. is related to different architectures of fault detection in which observed system symptoms are used to short-list the suspected components and then use reliability and other empirical data to assign fault probabilities to these suspect components.
None of the prior art considers the possibility of with calculating the residuals and matching them with individual fault models. Accordingly, it would be of great advantage if a system could be developed that uses a fault model based on prior experience, physics, and data analysis.
Another advantage would be to provide a system that uses pattern-matching techniques for fault diagnosis and isolation.
Yet another advantage would be to have a system that uses residual calculation and fault model matching.
A great advantage would be to have a system for detecting simultaneous occurrence of multiple faults.
Finally, an important advantage would be to have a system that is are able to diagnose the realistic faults such as turbine erosion, lube oil clogging and the like.
Other advantages and features will appear hereinafter.